Robust Malicious Network Traffic Detection Framework With Automated Drift Detection, Identification, and Adaptation

Summary

Argus is a framework for detecting malicious network traffic that addresses concept drift, where changing traffic patterns degrade static detectors. It combines a contrastive learning module for compact traffic representations, drift detection based on category-specific reconstruction loss distributions, and clustering to identify malicious drifting traffic without human intervention. The authors report an average F1 score above 95% across conditions, including extreme drift scenarios.